Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory680.2 B

Variable types

Text2
Numeric11
Categorical9
DateTime1

Alerts

avg_ads_per_hour is highly overall correlated with num_ads_seen_today and 1 other fieldsHigh correlation
budget_time_interaction is highly overall correlated with pca1 and 2 other fieldsHigh correlation
campaign_budget is highly overall correlated with pca2High correlation
click_rate_prior is highly overall correlated with pca2 and 1 other fieldsHigh correlation
cluster is highly overall correlated with time_spent_on_pageHigh correlation
num_ads_seen_today is highly overall correlated with avg_ads_per_hour and 1 other fieldsHigh correlation
pca1 is highly overall correlated with avg_ads_per_hour and 4 other fieldsHigh correlation
pca2 is highly overall correlated with campaign_budget and 2 other fieldsHigh correlation
previous_clicks is highly overall correlated with click_rate_prior and 1 other fieldsHigh correlation
time_spent_on_page is highly overall correlated with budget_time_interaction and 3 other fieldsHigh correlation
time_spent_on_page_log is highly overall correlated with budget_time_interaction and 2 other fieldsHigh correlation
budget_time_interaction has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
previous_clicks has 1337 (13.4%) zeros Zeros
click_rate_prior has 1337 (13.4%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:47:30.781925
Analysis finished2025-04-14 16:47:40.943377
Duration10.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct2889
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size641.1 KiB
2025-04-14T22:17:41.192112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.6325
Min length6

Characters and Unicode

Total characters86325
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique344 ?
Unique (%)3.4%

Sample

1st rowuser_861
2nd rowuser_1295
3rd rowuser_1131
4th rowuser_1096
5th rowuser_1639
ValueCountFrequency (%)
user_2687 12
 
0.1%
user_921 12
 
0.1%
user_19 11
 
0.1%
user_662 10
 
0.1%
user_1017 10
 
0.1%
user_2774 10
 
0.1%
user_2709 10
 
0.1%
user_2533 9
 
0.1%
user_1463 9
 
0.1%
user_2157 9
 
0.1%
Other values (2879) 9898
99.0%
2025-04-14T22:17:41.565848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 10000
11.6%
s 10000
11.6%
e 10000
11.6%
r 10000
11.6%
_ 10000
11.6%
2 6292
7.3%
1 6278
7.3%
9 3095
 
3.6%
8 3037
 
3.5%
6 3027
 
3.5%
Other values (5) 14596
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 10000
11.6%
s 10000
11.6%
e 10000
11.6%
r 10000
11.6%
_ 10000
11.6%
2 6292
7.3%
1 6278
7.3%
9 3095
 
3.6%
8 3037
 
3.5%
6 3027
 
3.5%
Other values (5) 14596
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 10000
11.6%
s 10000
11.6%
e 10000
11.6%
r 10000
11.6%
_ 10000
11.6%
2 6292
7.3%
1 6278
7.3%
9 3095
 
3.6%
8 3037
 
3.5%
6 3027
 
3.5%
Other values (5) 14596
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 10000
11.6%
s 10000
11.6%
e 10000
11.6%
r 10000
11.6%
_ 10000
11.6%
2 6292
7.3%
1 6278
7.3%
9 3095
 
3.6%
8 3037
 
3.5%
6 3027
 
3.5%
Other values (5) 14596
16.9%

age
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.7334
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:41.664319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median40
Q352
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.490965
Coefficient of variation (CV)0.33120153
Kurtosis-1.1763086
Mean40.7334
Median Absolute Deviation (MAD)11
Skewness0.037692195
Sum407334
Variance182.00613
MonotonicityNot monotonic
2025-04-14T22:17:41.754297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
32 261
 
2.6%
34 244
 
2.4%
30 238
 
2.4%
61 238
 
2.4%
43 236
 
2.4%
36 232
 
2.3%
31 228
 
2.3%
40 228
 
2.3%
27 226
 
2.3%
25 225
 
2.2%
Other values (37) 7644
76.4%
ValueCountFrequency (%)
18 216
2.2%
19 212
2.1%
20 225
2.2%
21 217
2.2%
22 202
2.0%
23 204
2.0%
24 209
2.1%
25 225
2.2%
26 222
2.2%
27 226
2.3%
ValueCountFrequency (%)
64 213
2.1%
63 191
1.9%
62 217
2.2%
61 238
2.4%
60 189
1.9%
59 202
2.0%
58 209
2.1%
57 201
2.0%
56 208
2.1%
55 210
2.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.6 KiB
Other
3388 
Female
3320 
Male
3292 

Length

Max length6
Median length5
Mean length5.0028
Min length4

Characters and Unicode

Total characters50028
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Other 3388
33.9%
Female 3320
33.2%
Male 3292
32.9%

Length

2025-04-14T22:17:41.847277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:41.918965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
other 3388
33.9%
female 3320
33.2%
male 3292
32.9%

Most occurring characters

ValueCountFrequency (%)
e 13320
26.6%
a 6612
13.2%
l 6612
13.2%
O 3388
 
6.8%
t 3388
 
6.8%
h 3388
 
6.8%
r 3388
 
6.8%
F 3320
 
6.6%
m 3320
 
6.6%
M 3292
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13320
26.6%
a 6612
13.2%
l 6612
13.2%
O 3388
 
6.8%
t 3388
 
6.8%
h 3388
 
6.8%
r 3388
 
6.8%
F 3320
 
6.6%
m 3320
 
6.6%
M 3292
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13320
26.6%
a 6612
13.2%
l 6612
13.2%
O 3388
 
6.8%
t 3388
 
6.8%
h 3388
 
6.8%
r 3388
 
6.8%
F 3320
 
6.6%
m 3320
 
6.6%
M 3292
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13320
26.6%
a 6612
13.2%
l 6612
13.2%
O 3388
 
6.8%
t 3388
 
6.8%
h 3388
 
6.8%
r 3388
 
6.8%
F 3320
 
6.6%
m 3320
 
6.6%
M 3292
 
6.6%

location
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size603.0 KiB
France
1551 
Germany
1445 
Japan
1423 
UK
1421 
US
1398 
Other values (2)
2762 

Length

Max length7
Median length6
Mean length4.7372
Min length2

Characters and Unicode

Total characters47372
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowBrazil
3rd rowIndia
4th rowBrazil
5th rowBrazil

Common Values

ValueCountFrequency (%)
France 1551
15.5%
Germany 1445
14.4%
Japan 1423
14.2%
UK 1421
14.2%
US 1398
14.0%
Brazil 1388
13.9%
India 1374
13.7%

Length

2025-04-14T22:17:41.990218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:42.095484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
france 1551
15.5%
germany 1445
14.4%
japan 1423
14.2%
uk 1421
14.2%
us 1398
14.0%
brazil 1388
13.9%
india 1374
13.7%

Most occurring characters

ValueCountFrequency (%)
a 8604
18.2%
n 5793
12.2%
r 4384
 
9.3%
e 2996
 
6.3%
U 2819
 
6.0%
i 2762
 
5.8%
F 1551
 
3.3%
c 1551
 
3.3%
G 1445
 
3.1%
m 1445
 
3.1%
Other values (10) 14022
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8604
18.2%
n 5793
12.2%
r 4384
 
9.3%
e 2996
 
6.3%
U 2819
 
6.0%
i 2762
 
5.8%
F 1551
 
3.3%
c 1551
 
3.3%
G 1445
 
3.1%
m 1445
 
3.1%
Other values (10) 14022
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8604
18.2%
n 5793
12.2%
r 4384
 
9.3%
e 2996
 
6.3%
U 2819
 
6.0%
i 2762
 
5.8%
F 1551
 
3.3%
c 1551
 
3.3%
G 1445
 
3.1%
m 1445
 
3.1%
Other values (10) 14022
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8604
18.2%
n 5793
12.2%
r 4384
 
9.3%
e 2996
 
6.3%
U 2819
 
6.0%
i 2762
 
5.8%
F 1551
 
3.3%
c 1551
 
3.3%
G 1445
 
3.1%
m 1445
 
3.1%
Other values (10) 14022
29.6%

device_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size618.3 KiB
Mobile
6018 
Desktop
3016 
Tablet
966 

Length

Max length7
Median length6
Mean length6.3016
Min length6

Characters and Unicode

Total characters63016
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowTablet
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile 6018
60.2%
Desktop 3016
30.2%
Tablet 966
 
9.7%

Length

2025-04-14T22:17:42.186267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:42.252706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mobile 6018
60.2%
desktop 3016
30.2%
tablet 966
 
9.7%

Most occurring characters

ValueCountFrequency (%)
e 10000
15.9%
o 9034
14.3%
b 6984
11.1%
l 6984
11.1%
M 6018
9.5%
i 6018
9.5%
t 3982
 
6.3%
D 3016
 
4.8%
s 3016
 
4.8%
k 3016
 
4.8%
Other values (3) 4948
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 9034
14.3%
b 6984
11.1%
l 6984
11.1%
M 6018
9.5%
i 6018
9.5%
t 3982
 
6.3%
D 3016
 
4.8%
s 3016
 
4.8%
k 3016
 
4.8%
Other values (3) 4948
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 9034
14.3%
b 6984
11.1%
l 6984
11.1%
M 6018
9.5%
i 6018
9.5%
t 3982
 
6.3%
D 3016
 
4.8%
s 3016
 
4.8%
k 3016
 
4.8%
Other values (3) 4948
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 9034
14.3%
b 6984
11.1%
l 6984
11.1%
M 6018
9.5%
i 6018
9.5%
t 3982
 
6.3%
D 3016
 
4.8%
s 3016
 
4.8%
k 3016
 
4.8%
Other values (3) 4948
7.9%

browser
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size611.5 KiB
Opera
2055 
Firefox
1994 
Chrome
1994 
Safari
1990 
Edge
1967 

Length

Max length7
Median length6
Mean length5.6005
Min length4

Characters and Unicode

Total characters56005
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSafari
2nd rowFirefox
3rd rowEdge
4th rowFirefox
5th rowSafari

Common Values

ValueCountFrequency (%)
Opera 2055
20.5%
Firefox 1994
19.9%
Chrome 1994
19.9%
Safari 1990
19.9%
Edge 1967
19.7%

Length

2025-04-14T22:17:42.326589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:42.395793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
opera 2055
20.5%
firefox 1994
19.9%
chrome 1994
19.9%
safari 1990
19.9%
edge 1967
19.7%

Most occurring characters

ValueCountFrequency (%)
r 8033
14.3%
e 8010
14.3%
a 6035
10.8%
o 3988
 
7.1%
f 3984
 
7.1%
i 3984
 
7.1%
p 2055
 
3.7%
O 2055
 
3.7%
F 1994
 
3.6%
x 1994
 
3.6%
Other values (7) 13873
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 8033
14.3%
e 8010
14.3%
a 6035
10.8%
o 3988
 
7.1%
f 3984
 
7.1%
i 3984
 
7.1%
p 2055
 
3.7%
O 2055
 
3.7%
F 1994
 
3.6%
x 1994
 
3.6%
Other values (7) 13873
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 8033
14.3%
e 8010
14.3%
a 6035
10.8%
o 3988
 
7.1%
f 3984
 
7.1%
i 3984
 
7.1%
p 2055
 
3.7%
O 2055
 
3.7%
F 1994
 
3.6%
x 1994
 
3.6%
Other values (7) 13873
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 8033
14.3%
e 8010
14.3%
a 6035
10.8%
o 3988
 
7.1%
f 3984
 
7.1%
i 3984
 
7.1%
p 2055
 
3.7%
O 2055
 
3.7%
F 1994
 
3.6%
x 1994
 
3.6%
Other values (7) 13873
24.8%
Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size634.9 KiB
2025-04-14T22:17:42.634425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcamp_109
2nd rowcamp_171
3rd rowcamp_162
4th rowcamp_135
5th rowcamp_195
ValueCountFrequency (%)
camp_120 126
 
1.3%
camp_144 126
 
1.3%
camp_124 125
 
1.2%
camp_193 123
 
1.2%
camp_140 120
 
1.2%
camp_127 119
 
1.2%
camp_154 116
 
1.2%
camp_135 116
 
1.2%
camp_148 116
 
1.2%
camp_175 114
 
1.1%
Other values (90) 8799
88.0%
2025-04-14T22:17:43.097876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11924
14.9%
c 10000
12.5%
a 10000
12.5%
m 10000
12.5%
p 10000
12.5%
_ 10000
12.5%
0 2065
 
2.6%
4 2045
 
2.6%
3 2041
 
2.6%
5 2013
 
2.5%
Other values (5) 9912
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11924
14.9%
c 10000
12.5%
a 10000
12.5%
m 10000
12.5%
p 10000
12.5%
_ 10000
12.5%
0 2065
 
2.6%
4 2045
 
2.6%
3 2041
 
2.6%
5 2013
 
2.5%
Other values (5) 9912
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11924
14.9%
c 10000
12.5%
a 10000
12.5%
m 10000
12.5%
p 10000
12.5%
_ 10000
12.5%
0 2065
 
2.6%
4 2045
 
2.6%
3 2041
 
2.6%
5 2013
 
2.5%
Other values (5) 9912
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11924
14.9%
c 10000
12.5%
a 10000
12.5%
m 10000
12.5%
p 10000
12.5%
_ 10000
12.5%
0 2065
 
2.6%
4 2045
 
2.6%
3 2041
 
2.6%
5 2013
 
2.5%
Other values (5) 9912
12.4%

ad_platform
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size627.5 KiB
Twitter
2574 
Google
2499 
Facebook
2497 
LinkedIn
2430 

Length

Max length8
Median length7
Mean length7.2428
Min length6

Characters and Unicode

Total characters72428
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLinkedIn
2nd rowGoogle
3rd rowGoogle
4th rowGoogle
5th rowFacebook

Common Values

ValueCountFrequency (%)
Twitter 2574
25.7%
Google 2499
25.0%
Facebook 2497
25.0%
LinkedIn 2430
24.3%

Length

2025-04-14T22:17:43.197843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:43.271246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
twitter 2574
25.7%
google 2499
25.0%
facebook 2497
25.0%
linkedin 2430
24.3%

Most occurring characters

ValueCountFrequency (%)
e 10000
13.8%
o 9992
13.8%
t 5148
 
7.1%
i 5004
 
6.9%
k 4927
 
6.8%
n 4860
 
6.7%
T 2574
 
3.6%
w 2574
 
3.6%
r 2574
 
3.6%
l 2499
 
3.5%
Other values (9) 22276
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10000
13.8%
o 9992
13.8%
t 5148
 
7.1%
i 5004
 
6.9%
k 4927
 
6.8%
n 4860
 
6.7%
T 2574
 
3.6%
w 2574
 
3.6%
r 2574
 
3.6%
l 2499
 
3.5%
Other values (9) 22276
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10000
13.8%
o 9992
13.8%
t 5148
 
7.1%
i 5004
 
6.9%
k 4927
 
6.8%
n 4860
 
6.7%
T 2574
 
3.6%
w 2574
 
3.6%
r 2574
 
3.6%
l 2499
 
3.5%
Other values (9) 22276
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10000
13.8%
o 9992
13.8%
t 5148
 
7.1%
i 5004
 
6.9%
k 4927
 
6.8%
n 4860
 
6.7%
T 2574
 
3.6%
w 2574
 
3.6%
r 2574
 
3.6%
l 2499
 
3.5%
Other values (9) 22276
30.8%

ad_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size618.5 KiB
Video
3390 
Carousel
3305 
Banner
3305 

Length

Max length8
Median length6
Mean length6.322
Min length5

Characters and Unicode

Total characters63220
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarousel
2nd rowBanner
3rd rowBanner
4th rowBanner
5th rowCarousel

Common Values

ValueCountFrequency (%)
Video 3390
33.9%
Carousel 3305
33.1%
Banner 3305
33.1%

Length

2025-04-14T22:17:43.352176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:43.420695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
video 3390
33.9%
carousel 3305
33.1%
banner 3305
33.1%

Most occurring characters

ValueCountFrequency (%)
e 10000
15.8%
o 6695
10.6%
a 6610
10.5%
r 6610
10.5%
n 6610
10.5%
V 3390
 
5.4%
i 3390
 
5.4%
d 3390
 
5.4%
C 3305
 
5.2%
u 3305
 
5.2%
Other values (3) 9915
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10000
15.8%
o 6695
10.6%
a 6610
10.5%
r 6610
10.5%
n 6610
10.5%
V 3390
 
5.4%
i 3390
 
5.4%
d 3390
 
5.4%
C 3305
 
5.2%
u 3305
 
5.2%
Other values (3) 9915
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10000
15.8%
o 6695
10.6%
a 6610
10.5%
r 6610
10.5%
n 6610
10.5%
V 3390
 
5.4%
i 3390
 
5.4%
d 3390
 
5.4%
C 3305
 
5.2%
u 3305
 
5.2%
Other values (3) 9915
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10000
15.8%
o 6695
10.6%
a 6610
10.5%
r 6610
10.5%
n 6610
10.5%
V 3390
 
5.4%
i 3390
 
5.4%
d 3390
 
5.4%
C 3305
 
5.2%
u 3305
 
5.2%
Other values (3) 9915
15.7%
Distinct6295
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2022-01-01 00:00:00
Maximum2023-02-21 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T22:17:43.496778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:43.594315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

clicked
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
8581 
1
1419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

Length

2025-04-14T22:17:43.683778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:43.742175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8581
85.8%
1 1419
 
14.2%

time_spent_on_page
Real number (ℝ)

High correlation 

Distinct4359
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.959068
Minimum0.01
Maximum162.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:43.815889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.0595
Q15.83
median13.855
Q327.3525
95-th percentile60.402
Maximum162.47
Range162.46
Interquartile range (IQR)21.5225

Descriptive statistics

Standard deviation20.038198
Coefficient of variation (CV)1.0039646
Kurtosis5.4899965
Mean19.959068
Median Absolute Deviation (MAD)9.455
Skewness2.0004034
Sum199590.68
Variance401.52937
MonotonicityNot monotonic
2025-04-14T22:17:43.914220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.94 11
 
0.1%
1.36 11
 
0.1%
0.54 10
 
0.1%
0.09 10
 
0.1%
4.73 10
 
0.1%
5.45 10
 
0.1%
0.5 10
 
0.1%
0.96 10
 
0.1%
2.17 9
 
0.1%
2.79 9
 
0.1%
Other values (4349) 9900
99.0%
ValueCountFrequency (%)
0.01 3
 
< 0.1%
0.02 4
 
< 0.1%
0.03 3
 
< 0.1%
0.04 4
 
< 0.1%
0.05 9
0.1%
0.06 6
0.1%
0.07 4
 
< 0.1%
0.08 5
0.1%
0.09 10
0.1%
0.1 3
 
< 0.1%
ValueCountFrequency (%)
162.47 1
< 0.1%
157.96 1
< 0.1%
148.24 1
< 0.1%
146.25 1
< 0.1%
145.92 1
< 0.1%
142.92 1
< 0.1%
138.95 1
< 0.1%
137.16 1
< 0.1%
136.65 1
< 0.1%
135.35 1
< 0.1%

num_ads_seen_today
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9792
Minimum0
Maximum17
Zeros70
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:43.997043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q36
95-th percentile9
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2426927
Coefficient of variation (CV)0.45041225
Kurtosis0.21246288
Mean4.9792
Median Absolute Deviation (MAD)2
Skewness0.45159176
Sum49792
Variance5.0296703
MonotonicityNot monotonic
2025-04-14T22:17:44.073065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 1794
17.9%
5 1722
17.2%
6 1450
14.5%
3 1434
14.3%
7 996
10.0%
2 819
8.2%
8 658
 
6.6%
9 380
 
3.8%
1 363
 
3.6%
10 186
 
1.9%
Other values (7) 198
 
2.0%
ValueCountFrequency (%)
0 70
 
0.7%
1 363
 
3.6%
2 819
8.2%
3 1434
14.3%
4 1794
17.9%
5 1722
17.2%
6 1450
14.5%
7 996
10.0%
8 658
 
6.6%
9 380
 
3.8%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
15 4
 
< 0.1%
13 9
 
0.1%
12 34
 
0.3%
11 79
 
0.8%
10 186
 
1.9%
9 380
 
3.8%
8 658
6.6%
7 996
10.0%

previous_clicks
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0358
Minimum0
Maximum10
Zeros1337
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:44.150540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4427496
Coefficient of variation (CV)0.70868928
Kurtosis0.44142838
Mean2.0358
Median Absolute Deviation (MAD)1
Skewness0.70588431
Sum20358
Variance2.0815265
MonotonicityNot monotonic
2025-04-14T22:17:44.220429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2678
26.8%
2 2630
26.3%
3 1848
18.5%
0 1337
13.4%
4 917
 
9.2%
5 400
 
4.0%
6 134
 
1.3%
7 47
 
0.5%
8 7
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 1337
13.4%
1 2678
26.8%
2 2630
26.3%
3 1848
18.5%
4 917
 
9.2%
5 400
 
4.0%
6 134
 
1.3%
7 47
 
0.5%
8 7
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 7
 
0.1%
7 47
 
0.5%
6 134
 
1.3%
5 400
 
4.0%
4 917
 
9.2%
3 1848
18.5%
2 2630
26.3%
1 2678
26.8%

campaign_budget
Real number (ℝ)

High correlation 

Distinct9948
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9986.7647
Minimum1532.6
Maximum19714.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:44.298284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1532.6
5-th percentile5917.1395
Q18293.7125
median9944.76
Q311660.722
95-th percentile14103.847
Maximum19714.69
Range18182.09
Interquartile range (IQR)3367.01

Descriptive statistics

Standard deviation2503.5062
Coefficient of variation (CV)0.25068241
Kurtosis0.0052714692
Mean9986.7647
Median Absolute Deviation (MAD)1686.48
Skewness0.05840946
Sum99867647
Variance6267543.4
MonotonicityNot monotonic
2025-04-14T22:17:44.393803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11800.65 3
 
< 0.1%
12388.87 2
 
< 0.1%
9572.27 2
 
< 0.1%
11504.41 2
 
< 0.1%
8765.93 2
 
< 0.1%
9970.45 2
 
< 0.1%
10135.18 2
 
< 0.1%
12058.49 2
 
< 0.1%
10898.47 2
 
< 0.1%
10111.11 2
 
< 0.1%
Other values (9938) 9979
99.8%
ValueCountFrequency (%)
1532.6 1
< 0.1%
1706.7 1
< 0.1%
1821.36 1
< 0.1%
2013.11 1
< 0.1%
2152.81 1
< 0.1%
2229.32 1
< 0.1%
2230.36 1
< 0.1%
2337.68 1
< 0.1%
2353.86 1
< 0.1%
2505.97 1
< 0.1%
ValueCountFrequency (%)
19714.69 1
< 0.1%
19446.18 1
< 0.1%
18819.45 1
< 0.1%
18650.39 1
< 0.1%
18508.31 1
< 0.1%
18396.18 1
< 0.1%
18322.98 1
< 0.1%
18237.37 1
< 0.1%
18211.03 1
< 0.1%
18208.17 1
< 0.1%

category
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size629.0 KiB
Finance
2049 
Gaming
2027 
Fashion
2006 
Electronics
1973 
Travel
1945 

Length

Max length11
Median length7
Mean length7.392
Min length6

Characters and Unicode

Total characters73920
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel
2nd rowTravel
3rd rowFinance
4th rowElectronics
5th rowFinance

Common Values

ValueCountFrequency (%)
Finance 2049
20.5%
Gaming 2027
20.3%
Fashion 2006
20.1%
Electronics 1973
19.7%
Travel 1945
19.4%

Length

2025-04-14T22:17:44.483241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:44.551329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
finance 2049
20.5%
gaming 2027
20.3%
fashion 2006
20.1%
electronics 1973
19.7%
travel 1945
19.4%

Most occurring characters

ValueCountFrequency (%)
n 10104
13.7%
i 8055
10.9%
a 8027
10.9%
c 5995
 
8.1%
e 5967
 
8.1%
F 4055
 
5.5%
o 3979
 
5.4%
s 3979
 
5.4%
l 3918
 
5.3%
r 3918
 
5.3%
Other values (8) 15923
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10104
13.7%
i 8055
10.9%
a 8027
10.9%
c 5995
 
8.1%
e 5967
 
8.1%
F 4055
 
5.5%
o 3979
 
5.4%
s 3979
 
5.4%
l 3918
 
5.3%
r 3918
 
5.3%
Other values (8) 15923
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10104
13.7%
i 8055
10.9%
a 8027
10.9%
c 5995
 
8.1%
e 5967
 
8.1%
F 4055
 
5.5%
o 3979
 
5.4%
s 3979
 
5.4%
l 3918
 
5.3%
r 3918
 
5.3%
Other values (8) 15923
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10104
13.7%
i 8055
10.9%
a 8027
10.9%
c 5995
 
8.1%
e 5967
 
8.1%
F 4055
 
5.5%
o 3979
 
5.4%
s 3979
 
5.4%
l 3918
 
5.3%
r 3918
 
5.3%
Other values (8) 15923
21.5%

time_spent_on_page_log
Real number (ℝ)

High correlation 

Distinct4359
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5965152
Minimum0.0099503309
Maximum5.0966295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:44.639581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0099503309
5-th percentile0.72246267
Q11.9213247
median2.6983365
Q33.3447152
95-th percentile4.1174424
Maximum5.0966295
Range5.0866792
Interquartile range (IQR)1.4233905

Descriptive statistics

Standard deviation1.0127296
Coefficient of variation (CV)0.39003414
Kurtosis-0.4369169
Mean2.5965152
Median Absolute Deviation (MAD)0.7040269
Skewness-0.33567972
Sum25965.152
Variance1.0256212
MonotonicityNot monotonic
2025-04-14T22:17:44.732245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6626879731 11
 
0.1%
0.858661619 11
 
0.1%
0.4317824164 10
 
0.1%
0.08617769624 10
 
0.1%
1.745715531 10
 
0.1%
1.864080131 10
 
0.1%
0.4054651081 10
 
0.1%
0.6729444732 10
 
0.1%
1.153731588 9
 
0.1%
1.332366019 9
 
0.1%
Other values (4349) 9900
99.0%
ValueCountFrequency (%)
0.009950330853 3
 
< 0.1%
0.0198026273 4
 
< 0.1%
0.02955880224 3
 
< 0.1%
0.03922071315 4
 
< 0.1%
0.04879016417 9
0.1%
0.05826890812 6
0.1%
0.06765864847 4
 
< 0.1%
0.07696104114 5
0.1%
0.08617769624 10
0.1%
0.0953101798 3
 
< 0.1%
ValueCountFrequency (%)
5.096629487 1
< 0.1%
5.068652598 1
< 0.1%
5.005555748 1
< 0.1%
4.992131823 1
< 0.1%
4.989888221 1
< 0.1%
4.96925759 1
< 0.1%
4.941285216 1
< 0.1%
4.928412434 1
< 0.1%
4.924714232 1
< 0.1%
4.915225109 1
< 0.1%

budget_time_interaction
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198694.37
Minimum93.2067
Maximum1865380.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:44.818588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum93.2067
5-th percentile10324.421
Q154402.587
median131971.48
Q3266909.28
95-th percentile620027.88
Maximum1865380.7
Range1865287.5
Interquartile range (IQR)212506.69

Descriptive statistics

Standard deviation210409.45
Coefficient of variation (CV)1.0589603
Kurtosis7.1919789
Mean198694.37
Median Absolute Deviation (MAD)92152.066
Skewness2.2340791
Sum1.9869437 × 109
Variance4.4272135 × 1010
MonotonicityNot monotonic
2025-04-14T22:17:44.915733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43761.306 1
 
< 0.1%
306266.2311 1
 
< 0.1%
92767.0094 1
 
< 0.1%
293495.6714 1
 
< 0.1%
191527.9758 1
 
< 0.1%
81279.0426 1
 
< 0.1%
9904.1696 1
 
< 0.1%
19039.5257 1
 
< 0.1%
458672.6876 1
 
< 0.1%
580781.1372 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
93.2067 1
< 0.1%
109.0271 1
< 0.1%
124.1146 1
< 0.1%
208.0072 1
< 0.1%
220.3671 1
< 0.1%
220.6718 1
< 0.1%
222.4136 1
< 0.1%
224.6178 1
< 0.1%
240.6172 1
< 0.1%
242.4804 1
< 0.1%
ValueCountFrequency (%)
1865380.688 1
< 0.1%
1787606.298 1
< 0.1%
1784235.301 1
< 0.1%
1767163.613 1
< 0.1%
1748621.428 1
< 0.1%
1702355.85 1
< 0.1%
1600500.83 1
< 0.1%
1587408.026 1
< 0.1%
1580038.003 1
< 0.1%
1568075.208 1
< 0.1%

avg_ads_per_hour
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20746667
Minimum0
Maximum0.70833333
Zeros70
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:44.995277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.083333333
Q10.125
median0.20833333
Q30.25
95-th percentile0.375
Maximum0.70833333
Range0.70833333
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.093445527
Coefficient of variation (CV)0.45041225
Kurtosis0.21246288
Mean0.20746667
Median Absolute Deviation (MAD)0.083333333
Skewness0.45159176
Sum2074.6667
Variance0.0087320665
MonotonicityNot monotonic
2025-04-14T22:17:45.071032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.1666666667 1794
17.9%
0.2083333333 1722
17.2%
0.25 1450
14.5%
0.125 1434
14.3%
0.2916666667 996
10.0%
0.08333333333 819
8.2%
0.3333333333 658
 
6.6%
0.375 380
 
3.8%
0.04166666667 363
 
3.6%
0.4166666667 186
 
1.9%
Other values (7) 198
 
2.0%
ValueCountFrequency (%)
0 70
 
0.7%
0.04166666667 363
 
3.6%
0.08333333333 819
8.2%
0.125 1434
14.3%
0.1666666667 1794
17.9%
0.2083333333 1722
17.2%
0.25 1450
14.5%
0.2916666667 996
10.0%
0.3333333333 658
 
6.6%
0.375 380
 
3.8%
ValueCountFrequency (%)
0.7083333333 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.625 4
 
< 0.1%
0.5416666667 9
 
0.1%
0.5 34
 
0.3%
0.4583333333 79
 
0.8%
0.4166666667 186
 
1.9%
0.375 380
 
3.8%
0.3333333333 658
6.6%
0.2916666667 996
10.0%

click_rate_prior
Real number (ℝ)

High correlation  Zeros 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40663432
Minimum0
Maximum6
Zeros1337
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:17:45.156317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16666667
median0.33333333
Q30.5
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0.33333333

Descriptive statistics

Standard deviation0.39644448
Coefficient of variation (CV)0.97494101
Kurtosis22.315539
Mean0.40663432
Median Absolute Deviation (MAD)0.16666667
Skewness3.1633511
Sum4066.3432
Variance0.15716822
MonotonicityNot monotonic
2025-04-14T22:17:45.247770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1337
 
13.4%
0.5 879
 
8.8%
0.3333333333 803
 
8.0%
0.25 666
 
6.7%
0.2 602
 
6.0%
0.4 502
 
5.0%
1 491
 
4.9%
0.1666666667 470
 
4.7%
0.2857142857 392
 
3.9%
0.6666666667 387
 
3.9%
Other values (61) 3471
34.7%
ValueCountFrequency (%)
0 1337
13.4%
0.0625 3
 
< 0.1%
0.07142857143 3
 
< 0.1%
0.07692307692 11
 
0.1%
0.08333333333 20
 
0.2%
0.09090909091 57
 
0.6%
0.1 97
 
1.0%
0.1111111111 162
 
1.6%
0.1176470588 1
 
< 0.1%
0.125 260
 
2.6%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 3
 
< 0.1%
4 5
 
0.1%
3.5 2
 
< 0.1%
3 17
 
0.2%
2.5 13
 
0.1%
2.333333333 1
 
< 0.1%
2 67
0.7%
1.75 10
 
0.1%
1.666666667 32
0.3%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7658765 × 10-17
Minimum-2.8271782
Maximum4.8710348
Zeros0
Zeros (%)0.0%
Negative5320
Negative (%)53.2%
Memory size78.2 KiB
2025-04-14T22:17:45.336609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.8271782
5-th percentile-1.52207
Q1-0.70647393
median-0.075068795
Q30.61329804
95-th percentile1.7713641
Maximum4.8710348
Range7.6982129
Interquartile range (IQR)1.319772

Descriptive statistics

Standard deviation1.017618
Coefficient of variation (CV)2.7022077 × 1016
Kurtosis0.77107679
Mean3.7658765 × 10-17
Median Absolute Deviation (MAD)0.65821131
Skewness0.55759697
Sum4.3942627 × 10-13
Variance1.0355465
MonotonicityNot monotonic
2025-04-14T22:17:45.435485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.006794382 1
 
< 0.1%
0.5000160369 1
 
< 0.1%
0.2155702169 1
 
< 0.1%
0.9406604699 1
 
< 0.1%
0.2110177412 1
 
< 0.1%
-0.4776621874 1
 
< 0.1%
-1.788933416 1
 
< 0.1%
-0.1993220135 1
 
< 0.1%
-0.6360812442 1
 
< 0.1%
0.1570476578 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-2.827178173 1
< 0.1%
-2.774734187 1
< 0.1%
-2.7304568 1
< 0.1%
-2.727446096 1
< 0.1%
-2.665944772 1
< 0.1%
-2.616856404 1
< 0.1%
-2.609018674 1
< 0.1%
-2.543344874 1
< 0.1%
-2.528666784 1
< 0.1%
-2.522328627 1
< 0.1%
ValueCountFrequency (%)
4.871034763 1
< 0.1%
4.834376731 1
< 0.1%
4.683085619 1
< 0.1%
4.642208586 1
< 0.1%
4.612864061 1
< 0.1%
4.598500763 1
< 0.1%
4.536181182 1
< 0.1%
4.486894373 1
< 0.1%
4.365002268 1
< 0.1%
4.267874463 1
< 0.1%

pca2
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3145041 × 10-17
Minimum-4.0551691
Maximum4.1924773
Zeros0
Zeros (%)0.0%
Negative5030
Negative (%)50.3%
Memory size78.2 KiB
2025-04-14T22:17:45.532109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.0551691
5-th percentile-1.6367329
Q1-0.68967426
median-0.0080358986
Q30.67006
95-th percentile1.6784057
Maximum4.1924773
Range8.2476464
Interquartile range (IQR)1.3597343

Descriptive statistics

Standard deviation1.0083715
Coefficient of variation (CV)7.6711172 × 1016
Kurtosis0.059151426
Mean1.3145041 × 10-17
Median Absolute Deviation (MAD)0.68002291
Skewness0.052494037
Sum1.6342483 × 10-13
Variance1.016813
MonotonicityNot monotonic
2025-04-14T22:17:45.622912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7368647193 1
 
< 0.1%
0.3826822486 1
 
< 0.1%
0.0825175641 1
 
< 0.1%
1.170332096 1
 
< 0.1%
-0.1146549627 1
 
< 0.1%
2.586738045 1
 
< 0.1%
-0.5236054558 1
 
< 0.1%
-0.5842055779 1
 
< 0.1%
-0.6522331871 1
 
< 0.1%
-0.04574178681 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-4.055169064 1
< 0.1%
-3.702054755 1
< 0.1%
-3.522635034 1
< 0.1%
-3.374468262 1
< 0.1%
-3.320628346 1
< 0.1%
-3.282586714 1
< 0.1%
-3.253851023 1
< 0.1%
-3.120715646 1
< 0.1%
-3.080004182 1
< 0.1%
-3.047727244 1
< 0.1%
ValueCountFrequency (%)
4.192477334 1
< 0.1%
4.135716218 1
< 0.1%
3.839229089 1
< 0.1%
3.699825566 1
< 0.1%
3.45257455 1
< 0.1%
3.275603452 1
< 0.1%
3.248676807 1
< 0.1%
3.206892096 1
< 0.1%
3.165997903 1
< 0.1%
3.165761173 1
< 0.1%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
2
3253 
1
3223 
0
2396 
3
1128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Length

2025-04-14T22:17:45.702961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:17:45.765865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Most occurring characters

ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3253
32.5%
1 3223
32.2%
0 2396
24.0%
3 1128
 
11.3%

Interactions

2025-04-14T22:17:39.864075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:31.794343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.670289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.456031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.312562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.049897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.841289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.672456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.458379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.230478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.097985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.922220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:31.853503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.737044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.520228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.373735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.116537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.901968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.737101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.524047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.291476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.160799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.991633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:31.923601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.811124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.594788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.444916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.192847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.973449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.813458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.600434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.363051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.233674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.059164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:31.990727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.885638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.666117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.514252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.266862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.042200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.887257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.673178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.433097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.306437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.120441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.053198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.954979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.734085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.574320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.336179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.106476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.955708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.741211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.498667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.370532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.187673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.119444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.028374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.805811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.643482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.407596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.175860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.029535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.813692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.569830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.444636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.248444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.180943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.096123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.874824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.705312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.476809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.336020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.096878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.880147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.634817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.510519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.316403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.249263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.170793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.948575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.776857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.555300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.408671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.170624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.954643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.707568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.588881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.383598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.477497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.247211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.021028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.844916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.632891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.477563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.245231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.024623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.899603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.661726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.445194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.541825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.318390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.173103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.918322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.703495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.543003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.315740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.093262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.965896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.731186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:40.512299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:32.612854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:33.391475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.248014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:34.987600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:35.776920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:36.611249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:37.392999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:38.166258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.036517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:17:39.802264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:17:45.833819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ad_platformad_typeageavg_ads_per_hourbrowserbudget_time_interactioncampaign_budgetcategoryclick_rate_priorclickedclusterdevice_typegenderlocationnum_ads_seen_todaypca1pca2previous_clickstime_spent_on_pagetime_spent_on_page_log
ad_platform1.0000.0090.0110.0000.0150.0210.0000.0000.0120.0000.0120.0000.0030.0000.0000.0000.0130.0000.0070.016
ad_type0.0091.0000.0000.0000.0000.0090.0140.0000.0000.0000.0160.0110.0000.0130.0000.0000.0000.0000.0000.012
age0.0110.0001.0000.0110.0000.0080.0050.008-0.0090.0000.4790.0150.0000.0220.0110.4810.0720.0010.0080.008
avg_ads_per_hour0.0000.0000.0111.0000.0000.0270.0070.000-0.4160.0230.4740.0000.0210.0001.0000.589-0.108-0.0020.0250.025
browser0.0150.0000.0000.0001.0000.0000.0000.0000.0150.0000.0000.0000.0070.0000.0000.0000.0160.0000.0000.017
budget_time_interaction0.0210.0090.0080.0270.0001.0000.2070.0090.0140.0000.4560.0000.0000.0000.0270.529-0.1090.0270.9680.968
campaign_budget0.0000.0140.0050.0070.0000.2071.0000.0000.0140.0000.0060.0210.0000.0130.0070.1240.7540.019-0.014-0.014
category0.0000.0000.0080.0000.0000.0090.0001.0000.0000.0150.0030.0000.0000.0000.0000.0190.0000.0220.0000.000
click_rate_prior0.0120.000-0.009-0.4160.0150.0140.0140.0001.0000.0070.1410.0000.0030.000-0.416-0.0030.5040.8790.0110.011
clicked0.0000.0000.0000.0230.0000.0000.0000.0150.0071.0000.0190.0000.0070.0000.0230.0110.0000.0000.0000.002
cluster0.0120.0160.4790.4740.0000.4560.0060.0030.1410.0191.0000.0130.0000.0000.4740.4770.1750.0150.5100.485
device_type0.0000.0110.0150.0000.0000.0000.0210.0000.0000.0000.0131.0000.0000.0120.0000.0150.0000.0000.0000.015
gender0.0030.0000.0000.0210.0070.0000.0000.0000.0030.0070.0000.0001.0000.0000.0210.0000.0000.0090.0000.000
location0.0000.0130.0220.0000.0000.0000.0130.0000.0000.0000.0000.0120.0001.0000.0000.0000.0000.0160.0110.000
num_ads_seen_today0.0000.0000.0111.0000.0000.0270.0070.000-0.4160.0230.4740.0000.0210.0001.0000.589-0.108-0.0020.0250.025
pca10.0000.0000.4810.5890.0000.5290.1240.019-0.0030.0110.4770.0150.0000.0000.5891.0000.0390.2810.5150.515
pca20.0130.0000.072-0.1080.016-0.1090.7540.0000.5040.0000.1750.0000.0000.000-0.1080.0391.0000.524-0.290-0.290
previous_clicks0.0000.0000.001-0.0020.0000.0270.0190.0220.8790.0000.0150.0000.0090.016-0.0020.2810.5241.0000.0220.022
time_spent_on_page0.0070.0000.0080.0250.0000.968-0.0140.0000.0110.0000.5100.0000.0000.0110.0250.515-0.2900.0221.0001.000
time_spent_on_page_log0.0160.0120.0080.0250.0170.968-0.0140.0000.0110.0020.4850.0150.0000.0000.0250.515-0.2900.0221.0001.000

Missing values

2025-04-14T22:17:40.618420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:17:40.843391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idagegenderlocationdevice_typebrowsercampaign_idad_platformad_typeimpression_dateclickedtime_spent_on_pagenum_ads_seen_todayprevious_clickscampaign_budgetcategorytime_spent_on_page_logbudget_time_interactionavg_ads_per_hourclick_rate_priorpca1pca2cluster
0user_86121OtherIndiaDesktopSafaricamp_109LinkedInCarousel2023-01-30 03:00:0004.44709856.15Travel1.69377943761.30600.2916670.000000-1.006794-0.7368650
1user_129523OtherBrazilTabletFirefoxcamp_171GoogleBanner2023-01-04 13:00:0009.648212015.31Travel2.364620115827.58840.3333330.222222-0.0381170.5348340
2user_113131FemaleIndiaMobileEdgecamp_162GoogleBanner2022-12-18 11:00:00014.032110611.26Finance2.710048148875.97780.0833330.333333-1.446593-0.0004271
3user_109621MaleBrazilMobileFirefoxcamp_135GoogleBanner2023-01-15 23:00:00015.306210233.56Electronics2.791165156573.46800.2500000.285714-0.537891-0.0143341
4user_163943MaleBrazilMobileSafaricamp_195FacebookCarousel2022-12-05 10:00:0009.745312677.12Finance2.373975123475.14880.2083330.5000000.0867811.3609322
5user_217064FemaleGermanyDesktopSafaricamp_144TwitterVideo2022-10-28 03:00:00017.655112483.00Fashion2.925846220324.95000.2083330.1666670.6432620.5331582
6user_46756OtherBrazilDesktopSafaricamp_105FacebookCarousel2022-07-31 01:00:00026.643111007.65Gaming3.319264293243.79600.1250000.2500000.055927-0.0124362
7user_123934FemaleUKMobileOperacamp_105GoogleVideo2023-02-12 23:00:00124.09419232.49Finance3.222469222410.68410.1666670.200000-0.590278-0.6732641
8user_33136MaleJapanMobileChromecamp_190GoogleVideo2023-01-25 20:00:0000.613212085.02Finance0.4762347371.86220.1250000.500000-1.1620211.0266501
9user_148323OtherBrazilDesktopFirefoxcamp_165GoogleVideo2022-03-23 08:00:00017.996213265.15Travel2.943913238640.04850.2500000.285714-0.2440430.8685681
user_idagegenderlocationdevice_typebrowsercampaign_idad_platformad_typeimpression_dateclickedtime_spent_on_pagenum_ads_seen_todayprevious_clickscampaign_budgetcategorytime_spent_on_page_logbudget_time_interactionavg_ads_per_hourclick_rate_priorpca1pca2cluster
9990user_78943FemaleUSDesktopSafaricamp_193GoogleVideo2022-06-26 20:00:0004.493212228.62Fashion1.70292854906.50380.1250000.500000-0.8016861.0409572
9991user_85045MaleUKMobileSafaricamp_137LinkedInCarousel2022-03-13 17:00:0007.633413217.02Electronics2.155245100845.86260.1250001.000000-0.2099992.0425852
9992user_222662FemaleFranceMobileFirefoxcamp_149LinkedInCarousel2022-05-20 00:00:000130.96407108.92Gaming4.882499930984.16320.1666670.0000003.285864-3.3744683
9993user_289031OtherIndiaMobileSafaricamp_173GoogleVideo2022-12-16 14:00:0003.941211994.42Electronics1.59736547258.01480.0416671.000000-1.7524181.0137691
9994user_137826MaleUKMobileEdgecamp_131TwitterVideo2022-03-24 09:00:0005.564112659.11Travel1.88099170384.65160.1666670.200000-1.2552650.6401781
9995user_117843FemaleBrazilDesktopChromecamp_189FacebookCarousel2022-01-08 07:00:00125.664112702.41Finance3.283164325943.84060.1666670.200000-0.0727790.3990752
9996user_195019MaleFranceMobileOperacamp_152GoogleVideo2023-01-07 11:00:00022.121212133.07Fashion3.140698268383.50840.0416671.000000-1.6028100.6794891
9997user_87828OtherUSMobileEdgecamp_147FacebookVideo2022-09-15 12:00:00025.32428548.26Electronics3.270329216441.94320.1666670.400000-0.597557-0.5610281
9998user_113934OtherGermanyMobileFirefoxcamp_180GoogleCarousel2022-09-12 17:00:00159.965112116.97Finance4.110218726533.52120.2083330.1666670.888456-0.4645233
9999user_157851OtherJapanMobileChromecamp_100GoogleBanner2022-04-01 02:00:0002.55907738.41Travel1.26694819732.94550.3750000.0000000.365718-1.2840610